Earlier quoted context omitted.
If you do the math (I did), in 2 years, open source models that you can run on a future MacBook Pro will be as capable as the frontier cloud models are today. Memory bandwidth is growing rapidly, as is the die area dedicated to the neural cores. And all the while, we have the silicon getting more power efficient and increasingly dense (as it always does). These hardware improvements are coming along as the open sourc…
A Opus 4.7/Gpt5.5 class model is 5 trillion parameters[1]. To run a 8 bit quantized version of that you need roughly 5TB of RAM. Today that is around 18 NVidia B300. That's around $900,000, without including the computers to run them in. It's true that the capability of open source models is improving, but running actual frontier models on your MPB seems a way off. [1] https://x.com/elonmusk/status/204212356166685523…
You could run it on a cluster of nodes that each do some mix of fetching parameters from disk and caching them in RAM. Use pipeline parallelism to minimize network bandwidth requirements given the huge size. Then time to first token may be a bit slow, but sustained inference should achieve enough throughput for a single user. That's a costly setup of course, but it doesn't cost $900k.